Systems based on artificial intelligence, using Natural Language Processing (NLP) and Natural Language Understanding (NLU) to interact with users/customers are highly coveted requirement nowadays. Conventional systems, which use Artificial Intelligence (AI) to decipher a user query content, are obsessed with user intent and its identification. User intent is the information pertaining to ‘what’ the user wants. As intelligent systems are evolving, resolving only the user intent may not suffice.
Intelligent systems using NLU, rely heavily on identifying key information in incoming user queries. The most vital information being the user intent. However, just finding the user intent is not enough to understand a user query in its entirety, especially in systems that require specific information, for example, the cause or reason of the intent, called the causal. Causal relates to the cause or reason of the user intent. When a person is applying for a leave on an automated system or voicing an instruction to his intelligent personal assistant, the cause of an instruction is vital. Therefore, a system, which extracts the causal, identifies the reason for a task or information conveyed by the user. Such a tool would be immensely helpful for intelligent systems, especially cognitive systems and man machine interface based systems.